Skip to main content
Glama

add_knowledge_refs

Add raw data references to a knowledge entry (links Layer 2 to Layer 3)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
refsYesReferences to raw data
projectIdYesProject ID
knowledgeIdYesKnowledge entry ID

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

C2.9/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description must carry the full transparency burden. It indicates a mutating operation but does not disclose whether references are appended or replaced, whether duplicates are allowed, which requirements exist (e.g., the knowledge entry must already exist), or what the response/return behavior is.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single well-front-loaded sentence with no filler. The key action is first, and the layer linkage context is a short parenthetical.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

This is a mutating tool with no annotations, no output schema, and only a one-sentence description. It lacks the behavioral and usage context an agent needs to know prerequisites, idempotency, side effects, and how it differs from the many sibling tools.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema covers all three parameters with descriptions, so the baseline is 3. The description adds the semantic that the references are 'raw data' and connect layers, but it does not provide meaningful detail beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description names a clear verb and resource: adding raw data references to a knowledge entry. The parenthetical 'links Layer 2 to Layer 3' gives some conceptual context, though those layer terms are not defined and the tool is not strongly differentiated from knowledge-related siblings such as link_knowledge or pin_knowledge.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance is given on when to use this tool rather than link_knowledge, add_knowledge, or other knowledge-related tools. The statement of what it does is present, but there are no conditions, exclusions, or alternative indicators.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Try in Browser

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

B3/5.0
Disambiguation3/5

Most tools target distinct resources (elements, knowledge, tasks, datasets, snapshots), but a few pairs blur boundaries: create_project/init_project both create projects, and pin_knowledge/set_knowledge_relevance both mark importance for future agents. The descriptions help separate them, but misselection is possible without careful reading.

Naming Consistency3/5

Tool names consistently use snake_case verb_noun and have solid list_/get_/search_ conventions. However creation verbs are inconsistent (add_element vs create_entry vs save_dataset vs init_project), and deletion mixes delete_entry/delete_file with remove_element, making the naming pattern less predictable than it could be.

Tool Count2/5

48 tools is well above the typical well-scoped range, and the set includes many lifecycle variants (create/init/save/add, delete/remove, update/set) that inflate the count. While the server covers a broad domain, the sheer number makes it heavy and harder for an agent to navigate.

Completeness3/5

The core surfaces (projects, elements, knowledge, timeline, tasks, chats, datasets, snapshots, files) have solid create/read/update coverage, with search and session-handoff tools. Notable gaps exist: read_file references a download path for binary files that no tool provides, and there is no get_entry or delete/archive for projects, datasets, snapshots, or chat sessions.

Resources